=Paper=
{{Paper
|id=None
|storemode=property
|title=Supporting Serendipitous and Focused Search
|pdfUrl=https://ceur-ws.org/Vol-909/poster12.pdf
|volume=Vol-909
|dblpUrl=https://dblp.org/rec/conf/eurohcir/Zhang12
}}
==Supporting Serendipitous and Focused Search==
https://ceur-ws.org/Vol-909/poster12.pdf
Supporting Serendipitous and Focused Search
Junte Zhang
Meertens Institute, Royal Netherlands Academy of Arts and Sciences
Amsterdam, the Netherlands
ABSTRACT resources and its technology.1 Descriptive metadata is used
People with complex information needs are for example Hu- to characterize large number of (legacy) research data re-
manities researchers, who need advanced search engines to sources (collections) and tools (e.g. Web services) to facili-
investigate their research questions. Much can be gained tate their management and discovery. The Search & Develop
by combining research datasets, reusing tools and serendipi- (S&D) project within CLARIN in the Netherlands uses the
tously discovering new insights for further research. Human- Component MetaData Infrastructure (CMDI; [4]) with ISO-
ities researchers have different (large-scale) research datasets cat [6, 12] to open up the sharing of resources and Web ser-
and tools, which are described differently with metadata. vices for people and machines first within the collections of
We present a highly interactive advanced search engine for a single institution, then across institutions in the Nether-
Humanities researchers that semantically converges differ- lands and eventually across Europe as whole. This infras-
ently structured metadata records from different collections tructure enables new research methods in language research
and institutions. It has features that support serendipitous and stimulates the Digital Humanities, where new insights
and focused search in context based on the structure of the can be gained by combining and reusing resources from dif-
metadata used. This single system serves Humanities re- ferent institutions and domains, and existing tools can be
searchers by allowing them to search interactively across yet more effectively found and reused based on new insights.
unexplored (research) data, discover patterns, locate rele- How to use the CMDI framework with ISOcat to search
vant data for new insights, and find existing tools that could for data and services, which can be understood by both peo-
provide novel use cases. ple from varying disciplines and machines? The challenge is
that the data is heterogenous both in content and struc-
ture, and can be massive in amount. In [11], we show how
Categories and Subject Descriptors to deal with such heterogeneously structured data in the
H.3.3 [Information Search and Retrieval]: Search pro- CMDI MI Search Engine. Users of the CMDI framework
cess; H.3.7 [Digital Libraries]: Systems issues, user is- are mostly Humanities researchers. What type of system is
sues; H.5.2 [Information interfaces and presentation]: needed driven by CMDI that matches with the search be-
Graphical user interfaces (GUI) havior of these users? This paper presents a proposition that
has been implemented on a live system.
General Terms
2. USING CMDI FOR FOCUSED AND SE-
Design, Human Factors
MANTIC ACCESS
CMDI has grown out of the need to facilitate access, re-
Keywords use, and interoperability using metadata [4]. A CMDI file
information retrieval, metadata, user interfaces, ehumanities in XML consists of a , , and . The former two are fixed in structure, while the
content and structure within is flexible and
1. INTRODUCTION can encapsulate any data in any structured form. An XML
The Common Language Resources and Technology In- schema can be used to make CMDI files coherent in struc-
frastructure (CLARIN) initiative seeks to establish an inte- ture for a (sub)collection and it contains references to ISOcat
grated and interoperable research infrastructure of language data categories (DC) stored in the Registry (DCR; [7, 6]).
The DCR was established by the ISO Technical Committee
37, Terminology and other language and content resources
based on the ISO 12620:2009 standard. Because multiple el-
ements may refer to the same DC, semantic interoperability
can be achieved across different datasets. A specification us-
ing the DCR and projected for example in an XML schema
is called a metadata profile and can be (re)used for describ-
Presented at EuroHCIR2012. Copyright c 2012 for the individual papers 1
by the papers’ authors. Copying permitted only for private and academic See http://www.clarin.eu/external/index.php?page=about-
purposes. This volume is published and copyrighted by its editors. clarin
(a) Query autocompletion based on the count that a query (b) The selection widget that allows users to keep overview of
occurs in a tag within the result set. By default the query box the search trail and change it, while updating the result list.
is content-centric, but searching directly in a tag is possible Here, the query stored is “periode” (period) within the tag
with Advanced Search (can be collapsed with a click). Users time coverage→description. Interesting terms are suggested
can express queries using the metadata or only the fulltext by presenting the top TF∗IDF terms, which people can use
of the document by discarding autocompletion. to start a parallel search episode.
(c) To further support query expansion and serendipitous in- (d) The distribution of retrieved time-referenced documents
formation seeking, a dynamic tag cloud is generated based (given the tags Century of Publication and Year of Publica-
on the last retrieved result list and used metadata label with tion) are visualized in bar or line charts. Users can click in
keyword highlighting. Moreover, retrieved geo-referenced the charts to narrow down the result set. The distribution of
documents are projected on a map and clustered by markers. results in tags collection and schema profile always appear.
Figure 1: The CMDI MI Search Engine (1).
ing datasets and for eventual access. Moreover, RELcat [10]
goes a step further by allowing for the storage of arbitrary
relationships between data categories to assist crosswalks
and to specify ontological relationships for further semantic
search, which in the future can be used in the CMDI MI
Search Engine using field collapsing.
We have indexed 246,728 CMDI files from 18 different pro-
files consisting of 143 different types of elements in a single
stream, which shows our indexing method for CMDI files is
robust enough to deal with complex data [11]. By indexing
metadata in CMDI on the XML element level, the search en-
gine can provide focused access [8]. We use straight-forward
information retrieval techniques only. The ‘Liederenbank’
(Dutch Song Database) alone has 9 different profiles (XML
schemas), which is equivalent to a sub-collection, ranging
from very differently structured descriptions about songs to
singers. How to provide interactive access to such heteroge-
neously structured data for Humanities researchers?
3. SERENDIPITY IN CONTEXT
When a user with no a priori intentions interacts with a
node of information and acquires useful information, then
serendipitous information retrieval occurs [9]. The success
of serendipitous discovery is not just the find itself, but be-
ing able or willing to do something with it, so that users get
more insight and can enhance the domain expertise [1]. Hu-
manities researchers are the type of users who can be greatly
(a) Retrieved list of results with the display of the list of re- supported in their research tasks with serendipitous IR, be-
sults with ‘fixed’ contextual information, snippets and key- cause their information-seeking behavior can be described
words in context within the last searched metadata label and as an idiosyncratic process of constant reading, “digging,”
the presentation of all used keywords in context given the
fulltext. There is links to the fulltext of the metadata record searching, and following leads [2]. This confirms with the
and the actual resource in the digital archive. Berrypicking model of [3], such as that queries are not static,
but rather evolve, and users “gather information in bits and
pieces instead of in one grand best retrieved set.”
Since the CMDI MI Search Engine should serve Humani-
ties researchers, we design it to support serendipitous search
and be highly interactive. The system has been designed to
maximize the user’s ability to explore. This is our focus.
The user interface of the system is depicted in Fig 1. It uses
the JavaScript library AJAX Solr2 , which has been heav-
ily modified and extended by us with JQuery. It allows for
faceted search [5] as we treat the indexed elements of the
CMDI files as one large category hierarchy.
A user can improving the search episode (session) by ef-
fectively reducing the information space step by step. These
steps are stored as part of the search trail, so the overview
is kept. There are different search strategies possible. Users
can search by fulltext by entering a query. This makes sure
users can always search in everything. The query get high-
lighted in context given the fulltext, but the dynamic tag
cloud widget that supports query expansion is not activated,
see Fig.1(a). Users can also do a focused search request by
using structure, i.e. within the content of a specified tag,
and get the content of these tags returned. This can be
(b) For each retrieved result in the list, there is a recom- content-centered, as users enter a keyword and the auto-
mendation (when available) of related results based on the completion widget returns a list consisting of keyword plus
content similarity of the last used metadata label. A recom- field name and hit count. It can also be structure-centered
mendation consist of a link to the record, the collection it (using the Advanced Search option) by looking up a tag and
belongs to, and a snippet (can be collapsed with a click). then entering a keyword also with the autocompletion fea-
ture. When the last two options are used, then the keyword
highlighting also occurs within the context of the retrieved
Figure 2: The CMDI MI Search Engine (2).
2
See https://github.com/evolvingweb/ajax-solr
snippets of the searched tag, see Fig.2(a). with very specific and complex information (research) needs.
A challenge is how we can support serendipitous search The search engine provides faceted search and has serendipi-
given the diversely structured metadata in CMDI. Hence, we tous features that maximize the user’s ability to explore any
introduce and propose the concept of serendipitous search in metadata in CMDI in context, such as query autocomple-
context. We can use the heterogeneous structure of different tion, tag clouds, and recommendation of related resources,
collections to provide context to the user in a single search while keeping track of the search trail. It is a tool that pro-
engine. We propose the following contextual system features vides interactive and focused access to heterogeneous meta-
that aim to support serendipitous and focused search. data, gives new perspectives on legacy (research) data and
tools, and provides new insights for research and develop-
• Help users by automatically completing the query that ment. It has been released as live, and can be used at
the user is entering while simultaneously and directly www.meertens.knaw.nl/cmdi/search.
giving the hit count for the suggested queries in con-
junction with a tag, see Fig.1(a). 5. ACKNOWLEDGMENTS
• Provide inline suggestions (Did you mean...) based on This work is part of the Search & Develop project at the
a spell checker whenever applicable. Meertens Institute, and funded by CLARIN-NL.
• Suggest a new parallel search episode (You could also 6. REFERENCES
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